Defect Engineering:Can it Mitigate Strong Coulomb Effect of Mg^(2+)in Cathode Materials for Rechargeable Magnesium Batteries?

Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.

Effects of combined drip irrigation and sub-surface pipe drainage on water and salt transport of saline-alkali soil in Xinjiang, China

Developing effective irrigation and drainage strategies to improve the quality of saline-alkali soil is vital for enhancing agricultural production and increasing economic returns. In this study, we explored how irrigation and drainage modes (flood irrigation, drip irrigation, and sub-surface pipe drainage under drip irrigation) improve the saline-alkali soil in Xinjiang, China. We aimed to study the transport characteristics of soil water and salt under different irrigation and drainage modes, and analyze the effects of the combination of irrigation and drainage on soil salt leaching, as well as its impacts on the growth of oil sunflower. Our results show that sub-surface pipe drainage under drip irrigation significantly reduced the soil salt content and soil water content at the 0–200 cm soil depth. Under sub-surface pipe drainage combined with drip irrigation, the mean soil salt content was reduced to below 10 g/kg after the second irrigation, and the soil salt content decreased as sub-surface pipe distance decreased. The mean soil salt content of flood irrigation exceeded 25 g/kg, and the mean soil desalination efficiency was 3.28%, which was lower than that of drip irrigation. The mean soil desalination rate under drip irrigation and sub-surface pipe drainage under drip irrigation was 19.30% and 58.12%, respectively. After sub-surface drainage regulation under drip irrigation, the germination percentage of oil sunflower seedlings was increased to more than 50%, which further confirmed that combined drip irrigation and sub-surface pipe drainage is very effective in improving the quality of saline-alkali soil and increasing the productivity of agricultural crops.

Heavy Metals Removal from Swine Wastewater Using Constructed Wetlands with Horizontal Sub-Surface Flow

The removal efficiency of Cu and Zn from swine wastewater was evaluated as effected by three variables: the hydraulic retention time (HRT) (24, 48, 72 and 96 hours), two different plant species (Typha domingensis Pers. and Eleocharis cellulosa) and two different sizes of filter media (5 and 15 mm) using a horizontal sub-surface flow constructed wetland. From the results, a significant difference was observed in the removal efficiency of Cu and Zn with respect to different hydraulic retention times. The best results were obtained in the HRT of 96 hours for Zn where 96% removal of Zn with Typha domingensis Pers. specie with gravel of 15 mm (experimental unit 6) was achieved. For Cu, at 72 hours of HRT, the efficiency was nearly 100% in five of the six study units (1, 2, 3, 5 and 6). In contrast, in experimental unit 4 with gravel of 15 mm and without plants, only 86% Cu removal was achieved.

A sub-surface eddy at inertial current layer in the Canada Basin,Arctic Ocean

An Arctic Ocean eddy in sub-surface layer is analyzed in this paper by use of temperature, salinity and current profiles data obtained at an ice camp in the Canada Basin during the second Chinese Arctic Expedition in summer of 2003. In the vertical temperature section, the eddy shows itself as an isolated cold water block at depth of 60 m with a minimum temperature of - 1.5℃, about 0.5℃ colder than the ambient water. Isopycnals in the eddy form a pattern of convex, which indicates the eddy is anticyclonic. Although maximum velocity near 0.4 m s^-1 occurs in the current records observed synchronously, the current pattern is far away from a typical eddy. By further analysis, inertial frequency osci/lations with amplitudes comparable with the eddy velocity are found in the sub-surface layer currents. After filter the inertial current and mean current, an axisymmetric current pattern of an eddy with maximum velocity radius of 5 km is obtained. The analysis of the T-S characteristics of the eddy core water and its ambient waters supports the conclusion that the eddy was formed on the Chukchi Shelf and migrated northeastward into the northern Canada Basin.

Sound Scattering From Rough Bubbly Ocean Surface Based on Modified Sea Surface Acoustic Simulator and Consideration of Various Incident Angles and Sub-surface Bubbles＇ Radii

The aim of the present study is to improve the capabilities and precision of a recently introduced Sea Surface Acoustic Simulator(SSAS) developed based on optimization of the Helmholtz–Kirchhoff–Fresnel(HKF) method. The improved acoustic simulator, hereby known as the Modified SSAS(MSSAS), is capable of determining sound scattering from the sea surface and includes an extended Hall–Novarini model and optimized HKF method. The extended Hall–Novarini model is used for considering the effects of sub-surface bubbles over a wider range of radii of sub-surface bubbles compared to the previous SSAS version. Furthermore, MSSAS has the capability of making a three-dimensional simulation of scattered sound from the rough bubbly sea surface with less error than that of the Critical Sea Tests(CST) experiments. Also, it presents scattered pressure levels from the rough bubbly sea surface based on various incident angles of sound. Wind speed, frequency, incident angle, and pressure level of the sound source are considered as input data, and scattered pressure levels and scattering coefficients are provided. Finally, different parametric studies were conducted on wind speeds, frequencies, and incident angles to indicate that MSSAS is quite capable of simulating sound scattering from the rough bubbly sea surface, according to the scattering mechanisms determined by Ogden and Erskine. Therefore, it is concluded that MSSAS is valid for both scattering mechanisms and the transition region between them that are defined by Ogden and Erskine.

Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional properties.However,the material reliability is significantly affected by various microscopic defects.In this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and quantified.Molecular dynamics(MD)simulations are employed to observe the failure evolution of different microscopic defects.The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion.At the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain points.The impact of defect types on the failure process is also discussed.Furthermore,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale model.The validity of the crack propagation model is confirmed through tensile tests.Finally,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD model.Our findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Optimizing the Interpretation of Sub-surface Resistivity in Relation with Borehole Productivity in Basement Area Applied to Seno Province （Burkina Faso）

This study aims at identifying possible correlations between shapes, types of geophysical anomalies and borehole productivity according to geological and hydrogeotogical contexts. The methodology adopted was a two-pronged one--the first step sought to： （1） interpret the electrical resistivity values from horizontal profiling and vertical electrical sounding implemented in Seno province that preceded the drilling of 513 boreholes; （ii） interpret data from pumping tests carried out on boreholes having a discharge superior to 1 m3/h （＂positive borehole＂） by using Cooper-Jacob＇s method. In the second step, according to geology, authors tried to identify possible correlations between each of the qualitative geophysical parameters： ~shape of anomaly〉〉, tttype of anomaly〉〉 and ~〈type curve〉〉 on the one hand, and hydrogeological parameters such as discharge, alteration thickness, transmissivity and saturated level on the other. The results of this study have shown that the chances of having a positive borehole in Seno province are higher when the type of anomaly is TCC （80%）, shape of anomaly is ＂W＂ and when type curve is ＂H＂ （80%） for all geological formations. Granitic formations are those that record higher discharges while schists record high transmissivity values.

Sub-Surface Drip Irrigation in Associated with H_(2)O_(2) Improved the Productivity of Maize under Clay-Rich Soil of Adana, Turkey

Maize being sub-tropical crop is sensitive to water deficit during the early growth stages;particularly clay-rich soil,due to the compaction of the soil.It is well-documented that potential sub-surface drip irrigation(SDI)(Full irrigation;SDIFull(100%field capacity(FC)),Deficit irrigation;SDIDeficit(70%FC))improves water use efficiency,which leads to increased crop productivity;since it has a constraint that SDI excludes soil air around the root-zone during irrigation events,which alter the root function and crop performance.Additionally,in clay-rich soils,the root system of plants generally suffers the limitation of oxygen,particularly the temporal hypoxia,and occasionally from root anoxia;while SDI system accomplishes with the aerating stream of irrigation in the rhizosphere could provide oxygen root environment.The oxygen can be introduced into the irrigation stream of SDI through two ways:the venturi principle,or by using solutions of hydrogen peroxide through the air injection system.Therefore,the application of hydrogen peroxide(H_(2)O_(2);HP)can mitigate the adverse effect of soil compactness and also lead to improving the growth,yield and yield attributes of maize in clay-rich soil.Considering the burning issue,a field study was conducted in consecutive two seasons of 2017 and 2018;where hybrid maize was cultivated as a second crop,to evaluate the effect of liquid-injection of H_(2)O_(2)(HP)into the irrigation stream of SDI on the performance of maize in a clay-rich soil field of Adana,Turkey.When soil water content decreased in 50%of avail-able water,irrigation was performed.The amount of water applied to reach the soil water content to the field capacity is SDIFull(100%FC)and 70%FC of this water is SDIDeficit(70%FC).In the irrigation program,hydrogen peroxide(HP)was applied at intervals of 7 days on average according to available water with and without HP:SDIFull(100%FC)+0 ppm HP with full SDI irrigation;SDIFull(100%FC)+250 ppm HP with deficit SDI irrigation;SDIDeficit(70%FC)+0 ppm HP,SDIDeficit(70%FC)+250 ppm HP and SDIDeficit(70%FC)+500 ppm HP.Deficit irrigation(SDIDeficit(70%FC))program was started from tasseling stage and continued up to the physiological maturity stage with sub-soil drip irrigation.H_(2)O_(2) was applied 3 times during the growing season.Two years’results revealed that the liquid-injection of H_(2)O_(2) into the irrigation stream of SDI improved the growth and yield-related attributes and grain yield of maize.Based on the obtained results,during the extreme climatic condition in the year 2017,SDIFull(100%FC)+250 ppm HP was more effective than SDIFull(100%FC)+0 ppm HP on all traits for relative to full irrigation.While,during the favourable climatic condition in the 2018 season,SDIFull(100%FC)+250 ppm HP was more effective than full irrigation with SDIFull(100%FC)+0 ppm HP for the grain yield,grains,and SPAD value.Accordingly,the most effective treatment was SDIFull(100%FC)+250 ppm HP,as it gave the highest growth and yield-related attributes and grain yield of maize followed by SDIDeficit(70%FC)+250 ppm HP.Therefore,SDIFull with 250 ppm H_(2)O_(2) using as liquid-injection may be recommended to mitigate the adverse effect of soil compactness particularly water-deficit stress in clay-rich soil for the sustainability of maize production.

Defect engineering in transition-metal(Fe,Co,andNi)-based electrocatalysts for water splitting

Electrocatalytic water splitting seems to be an efficient strategy to deal with increasingly serious environmental problems and energy crises but still suffers from the lack of stable and efficient electrocatalysts.Designing practical electrocatalysts by introducing defect engineering,such as hybrid structure,surface vacancies,functional modification,and structural distortions,is proven to be a dependable solution for fabricating electrocatalysts with high catalytic activities,robust stability,and good practicability.This review is an overview of some relevant reports about the effects of defect engineering on the electrocatalytic water splitting performance of electrocatalysts.In detail,the types of defects,the preparation and characterization methods,and catalytic performances of electrocatalysts are presented,emphasizing the effects of the introduced defects on the electronic structures of electrocatalysts and the optimization of the intermediates'adsorption energy throughout the review.Finally,the existing challenges and personal perspectives of possible strategies for enhancing the catalytic performances of electrocatalysts are proposed.An in-depth understanding of the effects of defect engineering on the catalytic performance of electrocatalysts will light the way to design high-efficiency electrocatalysts for water splitting and other possible applications.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Impact of Atrial Septal Defect Closure on Mortality in Older Patients

Background:Atrial septal defect(ASD)is a common form of adult congenital heart disease that can lead to long-term adverse outcomes if left untreated.Early closure of ASD has been associated with excellent outcomes and lower complication rates.However,there is limited evidence regarding the prognosis of ASD closure in older adults.This study aims to evaluate the mortality rates in older ASD patients with and without closure.Methods:A retrospective cohort study was conducted on patients aged 40 years or older with ASD between 2001 and 2017.Patients were followed up to assess all-cause mortality.Univariable and multivariable analyses were performed to identify the predictors of mortality.A p-value of<0.05 was considered statistically significant.Results:The cohort consisted of 450 patients(mean age 56.6±10.4 years,77.3%female),with 66%aged between 40 and 60 years,and 34%over 60 years.Within the cohort,299 underwent ASD closure(201 with transcatheter and 98 with surgical closure).During the median follow-up duration of 7.9 years,51 patients died.The unadjusted cumulative 10-year rate of mortality was 3%in patients with ASD closure,and 28%in patients without ASD closure(log-rank p<0.001).Multivariable analysis revealed that age(hazard ratio[HR]1.04,95%confidence interval[CI]1.006–1.06,p=0.01),NYHA class(HR 2.75,95%CI 1.63–4.62,p<0.001),blood urea nitrogen(BUN)(HR 1.07,95%CI 1.03–1.12,p<0.001),right ventricular systolic pressure(RVSP)(HR 1.07,95%CI 1.003–1.04,p=0.01),and lack of ASD closure(HR 15.12,95%CI 5.63–40.59,p<0.001)were independently associated with mortality.Conclusion:ASD closure demonstrated favorable outcomes in older patients.Age,NYHA class,BUN,RVSP,and lack of ASD closure were identified as independent factors linked to mortality in this population.